Method and device for designing a data network

ABSTRACT

A method of designing a transport network having a plurality of network elements and a plurality of connections between the network elements by (a) defining a first network configuration and at least one alternative network configuration for the same transport network; (b) calculating for each network configuration, a probability function representing, for each maximum number of routable flows, the probability of routing such a number of flows in the network configuration currently considered; (c) calculating for each network configuration, a unit-cost-per-flow function calculated as the ratio between a sum of the costs relative to the network elements of the network configuration currently considered and the probability function; and (d) comparing the unit-cost-per-flow functions of the network configurations considered, for choosing a network configuration having a lowest unit-cost-per-flow value.

CROSS REFERENCE TO RELATED APPLICATION

This application is a national phase application based onPCT/EP2002/014312, filed Dec. 16, 2002, the content of which isincorporated herein by reference.

FIELD OF THE INVENTION

The present invention refers to a method and a device for designing atransport network.

BACKGROUND ART

According to conventional communication network design methods, anoptimum design of a network is conducted only when a new network isplanned for the first time. During the design process, path and linkcapacities of a network are designed for a given specific trafficdemand. For a given predetermined demand pattern an optimisation of thenetwork design is conducted, and an optimum network minimizing the costis designed. Afterwards, as the demand pattern changes from the patternconsidered during the initial planning phase, it is likely that theefficiency of the network decreases day after day.

In actual telecommunication networks, indeed, the demand pattern variesvery frequently, depending upon changes in client subscriptions orchanges in traffic load due to the widespreading of new communicationservices or the introduction of new techniques.

It follows that, in a conventional network, forecast of the demand ishardly possible. Moreover, in multimedia networks of recent years,demand forecast has become more and more difficult.

Currently, the re-design of computer networks, particularly wide areanetworks, is a complex procedure requiring specialist trained staff. Fora nation-wide network, it may take a specialist several weeks to designand simulate an alternative network architecture.

Very often, as the traffic demand increases in correspondence of aparticular path or node of a network, a network administrator/operatorintervenes simply adding new apparatuses, in order to solve the problemvery rapidly. As a consequence the network configuration, as well as thearchitecture of single nodes, grows in an irregular, confused and veryexpensive way.

In U.S. Pat. No. 6,223,220 a method of designing a computer network isdisclosed making use of an object-based computer representation whichallows on-screen linking of a service object, representative of anetwork service, to site objects representative of physical sites on awide-area network. The user specifies expected traffic demands betweenthe sites, and an algorithm calculates a physical connectivity maprepresentative of proposed hardware circuits linking physical sites.

The method disclosed in U.S. Pat. No. 6,223,220 analyses the networkunder known traffic conditions, for determining a network configurationwhich is optimised for those particular conditions. The networkoptimisation obtained is therefore strictly connected to a particulartraffic condition, representative of a past traffic flow, and does notderive from a forecast of possible future traffic flows on the network.

The Applicant has tackled the problem of designing or optimising atransport network, or even a single network node, in order to satisfy anestimated future traffic demand on the network, with particularattention to realization and reorganization costs. To this purpose anetwork design and analysis method allowing to evaluate the flow's costfor different network structures is described.

In the following the term “flow” is intended as the allocation, on atransport network system, of an amount of structured bandwidth.

The Applicant observes that one of the main goals when atelecommunication network has to be planned or optimised, is tounderstand which is the best topology to adopt in order to satisfy thetraffic demand and, preferably, to save money.

The Applicant is of the opinion that, since one would hardly know howthe traffic demand will evolve in a network, it would be useful to anetwork administrator/designer to have a methodology able to show, fordifferent network topologies, the probability to satisfy client's needs.

In view of the above, it is an object of the invention to provide anetwork design and analysis method and device allowing to evaluate theflow's cost for different network topologies, allowing to understandwhich is the best configuration or evolution for the network understudy.

SUMMARY OF THE INVENTION

According to the invention that object is achieved by means of a methodand a device for designing a transport network, the network having aplurality of network elements and a plurality of connections connectingthe elements, comprising means for defining a plurality of alternativenetwork topologies to be analysed, means for evaluating, for eachalternative topology, a probability of satisfying a maximum number n offlows, and means for evaluating a flow's relative cost for thealternative topologies, allowing to understand which is the bestconfiguration or evolution for the network under study.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described, by way of example only, withreference to the annexed figures of drawing, wherein:

FIG. 1 is a flow diagram of a method of designing a transport networkrealized according to the present invention;

FIG. 2 is a detailed flow diagram of a probability evaluation routineused in the method shown in FIG. 1;

FIG. 3 is a block diagram of a first network topology considered forillustrating a method according to the present invention;

FIG. 4 is a block diagram of a second network topology considered forillustrating a method according to the present invention;

FIG. 5 is a graph showing a probability of satisfying a maximum number nof flows;

FIG. 6 is a graph showing a flow's unit cost as a function of theprobability of satisfying a maximum number n of flows; and

FIG. 7 is a graph showing comparison results of a method/device realizedaccording to the present invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION

With reference to the flow diagram of FIG. 1, a network design andanalysis method allowing to evaluate the flow's cost for differentnetwork structures will be described herein below.

As a first step (block 4 in diagram of FIG. 1) it is necessary to definea plurality of alternative network configurations, each configurationbeing defined by a plurality of network elements interconnected by aplurality of connections.

For each network under study, the following input data have to bespecified:

number of nodes/equipments inside the network;

number and typologies of the links (real or hypothetical) betweennodes/equipments.

A first network configuration can be, for example, the configuration ofan existing network which will be compared with a number of alternativenetwork configurations, in case the design method is used for optimisingan existing network or node; otherwise the method according to theinvention can be used as a design tool for designing a new networkarchitecture or even a single node of a network.

Once a plurality, at least two, of network configurations X have beendefined the methodology follows the following main steps:

evaluation (block 6 in FIG. 1) for the configurations under study, ofthe probability of satisfying a maximum number n of flows; this stepcalculates, for each alternative network configuration X_(i), aprobability function P(n) representing, for each maximum number n ofroutable flows, the probability of routing such a number of flows in thenetwork configuration currently considered;

evaluation (block 8 in FIG. 1) of a complexity value, representative ofa flow's unitary cost, for different network configurations; this stepcalculates, for each alternative network configuration, a complexityfunction C_(i)(n), corresponding, in terms of costs, to aunit-cost-per-flow function, calculated as the ratio between a sum ofcomplexity factors, or costs, relative to the network elements of thenetwork configuration currently considered and the probability functionP(n) previously obtained;

determination (block 10 in FIG. 1) of the best configuration; this stepis performed by comparing each other the complexity, orunit-cost-per-flow, functions C_(i)(n) of the alternative networkconfigurations, for choosing a network configuration having a lowestunit-cost-per-flow, or complexity, value.

The step of evaluation of the probability P(n) of routing at maximum nflows (block 6 in FIG. 1) will now be described in detail, withreference to FIG. 2.

Once the number of nodes/equipments and the number of links existingbetween them has been defined, in order to compare different networkstructures, such structures are analysed under random traffic conditionor under polarized traffic condition, depending from the features of thenetwork under study.

Considering a random traffic demand, a random number in the rangebetween 1 and the number of nodes/equipments of the network is generated(block 26 in FIG. 2); this number represents the flow's origin (block 22in FIG. 2). A second random number, belonging to the same range butdifferent to the first one, is then generated (block 28 in FIG. 2); thissecond number will be the flow's destination (block 24 in FIG. 2).

Otherwise, if a polarized traffic demand has to be considered, differentweights have to be assigned to one or more directions (a direction isthe path between origin and destination), in the traffic generator(blocks 26 and 28 of FIG. 2).

Once the flow's origin and the destination (blocks 22 and 24 of FIG. 2)have been defined, the process tries to route it on the cheapest path,that's to say on the path using only one hop (block 30 FIG. 2). If thispath is free, the flow is routed, a partial counter n_OK of maximumroutable flows is increased (block 32 FIG. 2), and a new flow isgenerated (routine 20 is re-executed until no more routable flows can befound). Otherwise, the process tries to route the same flow on a moreexpensive path (i.e. a two hops path, block 34 of FIG. 2, and so on, upto n-hops path, block 36 of FIG. 2. When it is not possible anymore toroute a path inside the network's structure under study, routine 20terminates, and a maximum number of flows routable n_OK is obtained.

The procedure of routine 20 is then repeated m times (loop 40 of FIG.2), where m is big enough in order to obtain a valid statistical data,for example m=50000. Each run of the routine generates a new valuen_OK(i), with i=1 . . . m.

The values n_OK(i) are used for calculating the probability functionP(n), as the empirical probability of an event can be defined as theratio between the number of favourable outcomes and the total number ofoutcomes:

${P(n)} = \frac{F(n)}{m}$

where F(n) is the frequency of each maximum number n of routable flows,and m is the number of times the procedure has been repeated. Themaximum number n of routable flows corresponds to the value n_OKpreviously determined.

The step previously described is repeated for every networkconfiguration X_(i) under evaluation, calculating, for eachconfiguration, a probability function P_(i)(n). In this way, the maximumnumber of flows routable inside the network is evaluated for all networkconfigurations.

Analysing the probability functions P_(i)(n) it is possible, asdescribed in detail in the following, to compare the different networkconfigurations. Once the probability of routing at maximum n flows hasbeen evaluated for any different structures of the network under study,in fact, it becomes possible to compare their costs and advantages.

In order to do that, the method considers the number of transmittingelements, or network elements, used in the different network'sstructures analysed and their relative complexity, or costs, forcalculating a complexity function C_(i)(n) representing a unitary costper flow for each network configuration. The function C_(i)(n), which isa function of the maximum number of routable flows, corresponds to aunit-cost-per-flow function and is defined as follows:

${C_{i}(n)} = \frac{\sum\limits_{j = 1}^{{n \cdot {transmitting}}\mspace{11mu}{{el}.}}{{transmitting}\mspace{14mu}{{{el}.(j)} \cdot {relative}}\mspace{11mu}{{cost}(j)}}}{P_{i}(n)}$

Function C_(i)(n) represents, for each alternative network configurationX_(i), the ratio between a sum of the costs (relativecost(j)) relativeto the network elements of the network configuration currentlyconsidered, which are proportional to the complexity of the same networkelements, and the corresponding probability function P_(i)(n).

By the comparison, for the different network configurations X_(i) understudy, of the values of the function C_(i)(n), it becomes possible tounderstand which is the best network configuration in terms ofcomplexity, and therefore in terms of costs (the configuration having alowest complexity, or unit-cost-per-flow, value).

In particular, in order to design or optimise the network in view offuture needs, in terms of maximum number of routable flows on thenetwork, the step of comparing the unit-cost-per-flow functions C_(i)(n)is performed calculating the function C_(i)(n) in correspondence of aparticular estimated maximum number n of routable flows. The estimatedmaximum number n of routable flows can be based, for example, on aforecast.

The method described above can be implemented in a device, whosestructure is as well represented by the diagram of FIG. 1, for designinga transport network comprising the following main units:

a network configuration unit 4, for defining a first networkconfiguration and at least one alternative network configuration;

a probability evaluation unit 6, for calculating, for each networkconfiguration X_(i), a probability function P_(i)(n) representing, foreach maximum number n of routable flows, the probability of routing sucha number of flows in the network configuration currently considered;

a complexity, or unit-cost-per-flow, evaluation unit 8, for calculating,for each network configuration, a complexity function C_(i)(n)calculated as the ratio between a sum of the costs relative to thenetwork elements of the network configuration currently considered X_(i)and the probability function P_(i)(n);

a comparison unit 10, for comparing the complexity functions C_(i)(n) ofthe network configurations, for choosing a network configuration havinga lowest complexity, or unit-cost-per-flow, value.

The method and device according to the present invention can beimplemented as a computer program comprising computer program code meansadapted to run on a computer. Such computer program can be embodied on acomputer readable medium.

In order to understand the operation of the method/device, an example ofapplication will now be described in detail. The alternative networkconfigurations considered are those shown in FIGS. 3 and 4, a Meshtopology and a Star topology connecting five Network Elements NE1 . . .NE5 inside a same node. It is supposed that, for each Network Element,24 ports are dedicated to connect the elements each other, in the waydescribed in FIGS. 3 and 4.

On the hypothesis of a random traffic demand, the values of theprobability P(n) of routing at maximum n flows for the two topologiesconsidered are shown in the graph diagram represented in FIG. 5. The twocurves 42, 44 represent, respectively, the probability to satisfy atmaximum n flows for the Mesh and for the Star topology.

Considering the relative costs shown in the following table for the maintransmitting elements of the network under study, it is possible toevaluate the flow's unit cost C(n) for the two topologies under study.The complexity factors of the transmitting elements are proportional tothe costs of the same transmitting elements.

Equipment Relative cost DXC 0.53 DXC's port 0.0004 Star's core 0.37 Portof the star's core 0.0002

The curves obtained for the flow's unit cost functions C(n) are shown inFIG. 6, wherein 46 represents the curve relative to the Mesh topologyand 48 represents the curve relative to the Star topology.

In order to correctly analyse the curves of FIG. 6, it is important tounderstand that, since the two topologies have a different range ofmaximum number n of flows routable, the comparison of the flow's unitcost C(n) has to be done in the region in which both probabilityfunctions P(n) are defined (i.e. where P(n)>0). In the case shown, thiscondition corresponds to values of the maximum number of flows routablen in the range between 52 and 60, as can be seen in FIG. 5.

The comparison of the flow's unit cost functions C(n) for the twotopologies under study, in the region 52<=n<=60, is shown in FIG. 7.

The chart of FIG. 7 can be used for determining three different regions,indicated by the black arrows 54, 56 and 58, showing different regionsof decision.

In particular the chart shows that if one knows that, inside the networkunder study, it will not be necessary to route more than 55 flows, theMesh topology will be more economical, on the other hand, if it will benecessary to route, at maximum, between 56 and 58 flows, the twotopologies will be equivalent, ultimately, if it will be necessary toroute more than 58 flows, the Star topology will be more convenient thanthe Mesh one.

1. A method of designing a transport network for routing a plurality ofroutable flows, said transport network having a plurality of networkelements and a plurality of connections between said network elements,the method comprising: a) defining a first network configuration and atleast one alternative network configuration for said transport network;b) calculating for each of said first and any alternative networkconfiguration, a probability function representing, for each maximumnumber of routable flows, the probability of routing such a number offlows in the network configuration currently considered, wherein saidprobability function is calculated as the ratio between the number oftimes that a maximum number of routable flows has been successfullyrouted by means of a test routine repeated a predetermined number oftimes, and the number of times said test routine has been repeated; c)calculating for each of said first and any alternative networkconfiguration, a complexity function calculated as the ratio between asum of complexity factors relative to the network elements of thenetwork configuration currently considered and said probabilityfunction; and d) comparing the complexity functions of said first andany alternative network configurations, for choosing a networkconfiguration having a lowest complexity value.
 2. The method as claimedin claim 1, wherein said test routine comprises: e) generating a firstrandom number representing a first network element; f) generating asecond random number, different from said first random number,representing a second network element; g) searching a free path betweensaid first network element and said second network element and, in casesaid free path has been found, increasing a counter of maximum routableflows and marking said path as a routed flow; and h) repeating steps (e)to (g) until no one free path can be found for routing a new flow. 3.The method as claimed in claim 2, wherein first and second randomnumbers are weighted random numbers in order to simulate a polarizedtraffic demand in the network.
 4. The method as claimed 2, wherein saidstep of searching a free path provides for searching initially ashortest path between said first and second network elements forsuccessively searching a longer path if said shortest path has not beenfound.
 5. The method as claimed in claim 1, wherein said step ofcomparing the complexity functions is performed calculating saidcomplexity function for each network configuration considered incorrespondence of an estimated maximum number of routable flows in saidtransport network.
 6. The method as claimed in claim 1, wherein thecomplexity factor of a network element is proportional to the cost ofthe same network element, and said complexity function represents aunit-cost-per-flow function.
 7. A computer readable medium comprisingcomputer program code executable by a computer, the computer programcode configured to perform a method of designing a transport network forrouting a plurality of routable flows, said transport network having aplurality of network elements and a plurality of connections betweensaid network elements, the method comprising: a) defining a firstnetwork configuration and at least one alternative network configurationfor said transport network; b) calculating for each of said first andany alternative network configuration, a probability functionrepresenting, for each maximum number of routable flows, the probabilityof routing such a number of flows in the network configuration currentlyconsidered, wherein said probability function is calculated as the ratiobetween the number of times that a maximum number of routable flows hasbeen successfully routed by means of a test routine repeated apredetermined number of times and the number of times said test routinehas been repeated; c) calculating for each of said first and anyalternative network configuration, a complexity function calculated asthe ratio between a sum of complexity factors relative to the networkelements of the network configuration currently considered and saidprobability function; and d) comparing the complexity functions of saidfirst and any alternative network configurations, for choosing a networkconfiguration having a lowest complexity value.
 8. A device fordesigning a transport network having a plurality of network elements anda plurality of connections between said network elements, the devicecomprising: a network configuration unit for defining a first networkconfiguration and at least one alternative network configuration forsaid transport network; a probability evaluation unit for calculatingfor each of said first and any alternative network configuration, aprobability function representing, for each maximum number of routableflows, the probability of routing such a number of flows in the networkconfiguration currently considered, wherein said probability evaluationunit calculates said probability function as the ratio between thenumber of times that a maximum number of routable flows has beensuccessfully routed by means of a test routine repeated a predeterminednumber of times and the number of times said test routine has beenrepeated; a complexity evaluation unit for calculating for each of saidfirst and any alternative network configuration, a complexity functioncalculated as the ratio between a sum of complexity factors relative tothe network elements of the network configuration currently consideredand said probability function; and a comparison unit for comparing thecomplexity functions of said first and any alternative networkconfigurations, for choosing a network configuration having a lowestcomplexity value.
 9. The device as claimed in claim 8, wherein said testroutine comprises the steps: generating a first random numberrepresenting a first network element; generating a second random numberdifferent from said first random number representing a second networkelement; searching a free path between said first network element andsaid second network element and, in case said free path has been found,increasing a counter of maximum routable flows and marking said path asa routed flow; repeating said steps until no one free path can be foundfor routing a new flow.
 10. The device as claimed in claim 9, whereinsaid step of searching a free path provides for searching initially ashortest path between said first and second network elements forsuccessively searching a longer path if said shortest path has not beenfound.
 11. The device as claimed in claim 8, wherein said comparisonunit compares the complexity functions by calculating said complexityfunction for each network configuration considered in correspondence ofan estimated maximum number of routable flows in said transport network.12. The device as claimed in claim 8 or 11, wherein the complexityfactor of a network element is proportional to the cost of the samenetwork element, and said complexity function represents aunit-cost-per-flow function.